【正文】 asymptotically equivalent to the homogeneous Poisson model. 離散與齊次泊松模型而不是使用齊次泊松模型，數(shù)據(jù)可以近似的離散泊松模型的研究區(qū)域劃分成許多小塊。由于碎片的數(shù)量增加至無(wú)限遠(yuǎn)，因此，其尺寸減小為零，離散泊松模型是漸近相當(dāng)于齊次泊松模型。與泊松模型，人口數(shù)據(jù)可能被指定在一個(gè)或幾個(gè)時(shí)間點(diǎn)，如人口普查年。與伯努利，時(shí)空置換，有序，指數(shù)和正常模式，一時(shí)間需要被指定為每一個(gè)案件和伯努利模型，為每個(gè)控制以及。Under the Poisson assumption, the likelihood function for a specific window is proportional to: where C is the total number of cases, c is the observed number of cases within the window, and E[c] is the covariate adjusted expected number of cases within the window under the nullhypothesis. Note that since the analysis is conditioned on the total number of cases observed, CE[c] is the expected number of cases outside the window. I() is an indicator function. When SaTScan is set to scan only for clusters with high rates, I() is equal to 1 when the window has more cases than expected under the nullhypothesis, and 0 otherwise. The opposite is true when SaTScan is set to scan only for clusters with low rates. When the program scans for clusters with either high or low rates, then I()=1 for all windows. The spacetime permutation model uses the same function as the Poisson model. Due to the conditioning on the marginals, the observed number of cases is only approximately Poisson distributed. Hence, it is no longer a formal likelihood ratio test, but it serves the same purpose as the test statistic. For the Bernoulli model the likelihood function is: where c and C are defined as above, n is the total number of cases and controls within the window, while N is the bined total number of cases and controls in the data set. The likelihood function for the multinomial, ordinal, exponential and normal models are more plex, due to the more plex nature of the data. We refer to the papers by Jung, Kulldorff, and Richards, Jung, Kulldorff and Klassen (2005)。 Kulldorff et al (2006)and Huang et al. for the likelihood functions for these models. The likelihood function for the spatial variation in temporal trends scan statistic is also more plex, as it involves the maximum likelihood estimation of several different trend functions. The likelihood function is maximized over all window locations and sizes, and the one with the maximum likelihood constitutes the most likely cluster. This is the cluster that is least likely to have occurred by chance. The likelihood ratio for this window constitutes the maximum likelihood ratio test statistic. Its distribution under the nullhypothesis is obtained by repeating the same analytic exercise on a large number of random replications of the data set generated under the null hypothesis. The pvalue is obtained through Monte Carlo hypothesis testing (Dwass, 1957), by paring the rank of the maximum likelihood from the real data set with the maximum likelihoods from the random data sets. If this rank is R, then p = R / (1 + simulation). In order for p to be a ‘nice looking’ number, the number of simulations is restricted to 999 or some other number ending in 999 such as 1999, 9999 or 99999. That way it is always clear whether to reject or not reject the null hypothesis for typical cutoff values such as , and . The SaTScan program scans for areas with high rates (clusters), for areas with low rates, or simultaneously for areas with either high or low rates. The latter should be used rather than running two separate tests for high and low rates respectively, in order to make correct statistical inference. The most mon analysis is to scan for areas with high rates, that is, for clusters. NonCompactness Penalty Function When the elliptic window shape is used, there is an option to use a nonpactness (eccentricity) penalty to favor more pact clusters. The main reason for this is that the elliptic scan statistic will under the null hypothesis typically generate an elliptic most likely cluster since there are more elliptic than circular clusters evaluated, and it will often be a long and narrow ellipse, since there are more of those. At the same time, the concept of clustering is based on a pactness criterion in the sense that the cases in the cluster should be close to each other, so we are more interested in pact clusters. When the nonpactness penalty is used, the pure likelihood ratio is no longer used as the test statistic. Rather, the test statistic is defined as the log likelihood ratio multiplied with a nonpactness penalty of the form [4s/(s+1)2]a, where s is the elliptic window shape defined as the ratio of the length of the longest to the shortest axis of the ellipse. For the circle, s=1. The parameter a is a penalty tuning parameter. With a=0, the penalty function is always 1 irrespectively of s, so that there is never a penalty. When a goes to infinity, the penalty function goes to 0 for all s1, so that only circular clusters are considered. Other than this, there is no clear intuitive meaning of the penalty tuning parameter a. In SaTScan, it is possible to use either a strong penalty (a=1) or a medium size penalty (a=1/2). 似然比檢驗(yàn)對(duì)于每一個(gè)位置和大小的掃描窗口，替代假設(shè)是，有一個(gè)高風(fēng)險(xiǎn)的窗口相比，外。泊松假設(shè)下，似然函數(shù)的一個(gè)特定的窗口是成正比：哪里是總?cè)藬?shù)的情況下，是觀察到的一些案件在窗口，和[ J ]是變量調(diào)整預(yù)期的案件數(shù)量在窗下的零假設(shè)。i()是功能的一個(gè)指標(biāo)。時(shí)則相反，SaTS can設(shè)置掃描只集群與低利率。時(shí)空置換模式使用相同的功能如泊松模型。因此，它不再是一個(gè)正式的似然比檢驗(yàn)，但具有同樣目的的檢驗(yàn)統(tǒng)計(jì)。似然函數(shù)的多項(xiàng)式，有序，指數(shù)和正常模式更為復(fù)雜，由于更復(fù)雜的數(shù)據(jù)的性質(zhì)。這些模型的似然函數(shù)。似然函數(shù)最大化的所有窗口的位置和大小，和一個(gè)與最大似然是最可能的集群。似然比這個(gè)窗口的最大似然比檢驗(yàn)統(tǒng)計(jì)。值獲得通過(guò)蒙特卡洛檢驗(yàn)假設(shè)（數(shù)據(jù)，1957），通過(guò)比較等級(jí)的最大似然從實(shí)際數(shù)據(jù)集的最大可能從隨機(jī)數(shù)據(jù)集。以磷是一個(gè)不錯(cuò)的數(shù)字，模擬次數(shù)限制為999或其他數(shù)字999結(jié)束，如1999，9999或99999。該SaTS can程序掃描率高的地區(qū)（集群），地區(qū)與低利率，同時(shí)或地區(qū)或者高或低利率。最常見(jiàn)的分析掃描率高的地區(qū)，這是，集群。主要原因是，橢圓掃描統(tǒng)計(jì)的零假設(shè)下通常產(chǎn)生一個(gè)橢圓最可能的集群因?yàn)橛懈嗟臋E圓形圓形集群和評(píng)價(jià)，它往往是一個(gè)狹長(zhǎng)的橢圓形，因?yàn)橛懈嗟娜?。?dāng)使用非緊刑罰，純?nèi)槐炔辉偈怯脕?lái)作為檢驗(yàn)統(tǒng)計(jì)。為圓，= 1。一個(gè)= 0，罰函數(shù)總是1無(wú)關(guān)的，所以不會(huì)有一個(gè)點(diǎn)球。除此之外，也沒(méi)有明確的直觀意義的罰款調(diào)整參數(shù)在SaTS can，就可以使用任何一個(gè)強(qiáng)大的刑罰（= 1）或中分享到 翻譯結(jié)果重試抱歉，系統(tǒng)響應(yīng)超時(shí)，請(qǐng)稍后再試 支持網(wǎng)頁(yè)翻譯，在輸入框輸入網(wǎng)頁(yè)地址即可 將幾乎永遠(yuǎn)是一個(gè)二次叢集，幾乎是相同的最可能的集群和幾乎一樣高的似然值，因?yàn)閿U(kuò)大或減少的簇大小只有輕微不改變的可能性很。也可能是次要集群不重疊，最有可能的集群，他們可能是一個(gè)很大的興趣。默認(rèn)的是地理上的重疊群不報(bào)告。Adjusting for More Likely Clusters When there are multiple clusters in the data set, the secondary clusters are evaluated as if there were no other